Hands-on Workshop: Implementing AI Use Cases with Industrial Data Training Course

Introduction and Team Use Case Selection

• Overview of AI applications in industrial settings.
• Categories of use cases: quality, maintenance, energy, and logistics.
• Formation of teams and definition of project goals.

Understanding and Preparing Industrial Data

• Types of industrial data: time-series, tabular, image, and text.
• Processes for data acquisition, cleaning, and preprocessing.
• Exploratory data analysis using Pandas and Matplotlib.

Model Selection and Prototyping

• Selecting between regression, classification, clustering, or anomaly detection.
• Training and evaluating models using Scikit-learn.
• Utilizing TensorFlow or PyTorch for advanced modeling tasks.

Visualizing and Interpreting Results

• Development of intuitive dashboards or reports.
• Interpretation of performance metrics (accuracy, precision, recall).
• Documentation of assumptions and limitations.

Deployment Simulation and Feedback

• Simulation of edge and cloud deployment scenarios.
• Gathering feedback and refining models.
• Strategies for integrating solutions into operational workflows.

Capstone Project Development

• Finalization and testing of team prototypes.
• Peer reviews and collaborative debugging sessions.
• Preparation of project presentations and technical summaries.

Team Presentations and Wrap-Up

• Presentation of AI solution concepts and achieved outcomes.
• Group reflection and discussion of lessons learned.
• Strategic roadmap for scaling use cases within the organization.

Summary and Next Steps